Create an Azure AI studio inference endpoint Generally available; Added in 8.14.0

PUT /_inference/{task_type}/{azureaistudio_inference_id}

Api key auth Basic auth Bearer auth

Create an inference endpoint to perform an inference task with the azureaistudio service.

Required authorization

Cluster privileges: manage_inference

Path parameters

task_type string

The type of the inference task that the model will perform.

Values are completion or text_embedding.
azureaistudio_inference_id string Required

The unique identifier of the inference endpoint.

Query parameters

timeout string

Specifies the amount of time to wait for the inference endpoint to be created.

External documentation

application/json

Body Required

chunking_settings object

The chunking configuration object.

External documentation
Hide chunking_settings attributes Show chunking_settings attributes object
- max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
- overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
- sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
- strategy string
  
  The chunking strategy: sentence or word.
  
  Default value is sentence.
service string Required

The type of service supported for the specified task type. In this case, azureaistudio.

Value is azureaistudio.
service_settings object Required

Settings used to install the inference model. These settings are specific to the openai service.
Hide service_settings attributes Show service_settings attributes object
- api_key string Required
  
  A valid API key of your Azure AI Studio model deployment. This key can be found on the overview page for your deployment in the management section of your Azure AI Studio account.
  
  IMPORTANT: You need to provide the API key only once, during the inference model creation. The get inference endpoint API does not retrieve your API key. After creating the inference model, you cannot change the associated API key. If you want to use a different API key, delete the inference model and recreate it with the same name and the updated API key.
  
  External documentation
- endpoint_type string Required
  
  The type of endpoint that is available for deployment through Azure AI Studio: token or realtime. The token endpoint type is for "pay as you go" endpoints that are billed per token. The realtime endpoint type is for "real-time" endpoints that are billed per hour of usage.
  
  External documentation
- target string Required
  
  The target URL of your Azure AI Studio model deployment. This can be found on the overview page for your deployment in the management section of your Azure AI Studio account.
- provider string Required
  The model provider for your deployment. Note that some providers may support only certain task types. Supported providers include:
  
  cohere - available for text_embedding and completion task types
  
  databricks - available for completion task type only
  
  meta - available for completion task type only
  
  microsoft_phi - available for completion task type only
  
  mistral - available for completion task type only
  
  openai - available for text_embedding and completion task types
- rate_limit object
  
  This setting helps to minimize the number of rate limit errors returned from Azure AI Studio. By default, the azureaistudio service sets the number of requests allowed per minute to 240.
  Hide rate_limit attribute Show rate_limit attribute object
  
  requests_per_minute number
  
  The number of requests allowed per minute.
task_settings object

Settings to configure the inference task. These settings are specific to the task type you specified.
Hide task_settings attributes Show task_settings attributes object
- do_sample number
  
  For a completion task, instruct the inference process to perform sampling. It has no effect unless temperature or top_p is specified.
- max_new_tokens number
  
  For a completion task, provide a hint for the maximum number of output tokens to be generated.
  
  Default value is 64.
- temperature number
  
  For a completion task, control the apparent creativity of generated completions with a sampling temperature. It must be a number in the range of 0.0 to 2.0. It should not be used if top_p is specified.
- top_p number
  
  For a completion task, make the model consider the results of the tokens with nucleus sampling probability. It is an alternative value to temperature and must be a number in the range of 0.0 to 2.0. It should not be used if temperature is specified.
- user string
  
  For a text_embedding task, specify the user issuing the request. This information can be used for abuse detection.

Responses

200 application/json
Hide response attributes Show response attributes object
- chunking_settings object
  
  Chunking configuration object
  
  Hide chunking_settings attributes Show chunking_settings attributes object
  
  max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
  
  overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
  
  sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
  
  strategy string
  
  The chunking strategy: sentence or word.
  
  Default value is sentence.
- service string Required
  
  The service type
- service_settings object Required
  
  Settings specific to the service
- task_settings object
  
  Task settings specific to the service and task type
- inference_id string Required
  
  The inference Id
- task_type string Required
  
  The task type
  
  Values are text_embedding or completion.

PUT /_inference/{task_type}/{azureaistudio_inference_id}

PUT _inference/text_embedding/azure_ai_studio_embeddings
{
    "service": "azureaistudio",
    "service_settings": {
        "api_key": "Azure-AI-Studio-API-key",
        "target": "Target-Uri",
        "provider": "openai",
        "endpoint_type": "token"
    }
}

resp = client.inference.put(
    task_type="text_embedding",
    inference_id="azure_ai_studio_embeddings",
    inference_config={
        "service": "azureaistudio",
        "service_settings": {
            "api_key": "Azure-AI-Studio-API-key",
            "target": "Target-Uri",
            "provider": "openai",
            "endpoint_type": "token"
        }
    },
)

const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "azure_ai_studio_embeddings",
  inference_config: {
    service: "azureaistudio",
    service_settings: {
      api_key: "Azure-AI-Studio-API-key",
      target: "Target-Uri",
      provider: "openai",
      endpoint_type: "token",
    },
  },
});

response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "azure_ai_studio_embeddings",
  body: {
    "service": "azureaistudio",
    "service_settings": {
      "api_key": "Azure-AI-Studio-API-key",
      "target": "Target-Uri",
      "provider": "openai",
      "endpoint_type": "token"
    }
  }
)

$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "azure_ai_studio_embeddings",
    "body" => [
        "service" => "azureaistudio",
        "service_settings" => [
            "api_key" => "Azure-AI-Studio-API-key",
            "target" => "Target-Uri",
            "provider" => "openai",
            "endpoint_type" => "token",
        ],
    ],
]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"azureaistudio","service_settings":{"api_key":"Azure-AI-Studio-API-key","target":"Target-Uri","provider":"openai","endpoint_type":"token"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/azure_ai_studio_embeddings"

client.inference().put(p -> p
    .inferenceId("azure_ai_studio_embeddings")
    .taskType(TaskType.TextEmbedding)
    .inferenceConfig(i -> i
        .service("azureaistudio")
        .serviceSettings(JsonData.fromJson("{\"api_key\":\"Azure-AI-Studio-API-key\",\"target\":\"Target-Uri\",\"provider\":\"openai\",\"endpoint_type\":\"token\"}"))
    )
);

Request examples

Run `PUT _inference/text_embedding/azure_ai_studio_embeddings` to create an inference endpoint that performs a text_embedding task. Note that you do not specify a model here, as it is defined already in the Azure AI Studio deployment.

{
    "service": "azureaistudio",
    "service_settings": {
        "api_key": "Azure-AI-Studio-API-key",
        "target": "Target-Uri",
        "provider": "openai",
        "endpoint_type": "token"
    }
}

Run `PUT _inference/completion/azure_ai_studio_completion` to create an inference endpoint that performs a completion task.

{
    "service": "azureaistudio",
    "service_settings": {
        "api_key": "Azure-AI-Studio-API-key",
        "target": "Target-URI",
        "provider": "databricks",
        "endpoint_type": "realtime"
    }
}